Comparisons of prototype- and exemplar-based neural network models of categorization using the GECLE framework

In the present study, GECLE (Matsuka, 2003) was used as a general modeling framework to systematically compare the plausibility of two prominent assumptions about internal representations of neural network (NN) models of human category learning. In particular, exemplar-model friendly Medin and Schaffer’s 5/4 stimulus set (1978) was used for comparing prototypeand exemplar-based NN models. The results indicate that some prototype-based models performed as good as or better than an exemplar-based model in replicating the empirical classification profile. In addition, a phenomenon called A2 advantage (i.e., people tend to categorize the less “prototypical” stimulus A2 more accurately than more “prototypical” stimulus A1) reported in empirical studies (e.g., Medin & Schaffer 1978) was also successfully reproduced by these prototype-based NN models.